Like statistical models, backbone extraction gives a compressed picture of a network. It can tell us many things about the original data, but it is not a model per se without an associated decompression algorithm. In this paper, we develop and investigate such algorithms—to generate surrogate networks from a network backbone [1]}, [2]}. These algorithms thus model the rest of the network, apart from the backbone (See Figure REF ). In particular, we consider surrogate network construction from temporal network backbones, and investigate how to recreate the same behavior
as the original temporal network with respect to the outcome of epidemic models unfolding on the network (note that this is a distinct problem from the one of inferring what contact network is the most likely to correspond to an observed empirical spreading pattern [3]}). Temporal networks encode not only which nodes are connected but also when the interactions between them happen [4]}, [5]}, [6]}, and several studies have pointed out the necessity of including realistic topological and temporal structures to correctly describe spreading dynamics on temporal networks, including epidemic, opinion or information spreading [7]}, [5]}.
The performance of surrogate data in correctly reproducing the outcome of spreading dynamics might thus be influenced by how such structures are taken into account in the backbone and surrogate data.
